Provide generalized aggregation #4
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This implements a generalized method for aggregating time-series data. Data can be aggregated over week or month intervals with a variety of aggregation methods to choose from. This will be useful for providing chart views at different levels (such as two-year periods vs. just showing the last month). Additionally, the generalized form of aggregation can be used to smooth out graphs where the sampling frequency changed with an update to Hubble Enterprise. The aggregation is done by splitting the time data into subsequent, gapless periods of time (weeks starting with Mondays or months), for each of which the aggregated values are then computed and returned. Aggregation methods define how to aggregate the values within individual time periods. The following aggregation methods are supported: - sum - mean - min - max - first (the chronologically first available value for that period) - last - median Additionally, periods at the beginning or the end of the time series may or may not be included if they are incomplete (there isn’t data for each day in the period). This is controlled by the setting includeIncomplete, which supports the following values: - none - start (includes an incomplete period at the beginning of the series) - end - both Finally, the pull request usage chart is changed to make use of the new aggregation facilities to reduce the granularity from daily to monthly data for now. This might be changed when we implement detail views. I also added several unit tests to check the aggregation methods (for off-by-one errors in particular).
Codecov Report
@@ Coverage Diff @@
## master #4 +/- ##
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+ Coverage 11.78% 23.83% +12.05%
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Files 1 1
Lines 280 323 +43
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+ Hits 33 77 +44
+ Misses 247 246 -1
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This implements a generalized method for aggregating time-series data.
Data can be aggregated over week or month intervals with a variety of
aggregation methods to choose from.
This will be useful for providing chart views at different levels (such
as two-year periods vs. just showing the last month). Additionally, the
generalized form of aggregation can be used to smooth out graphs where
the sampling frequency changed with an update to Hubble Enterprise.
The aggregation is done by splitting the time data into subsequent,
gapless periods of time (weeks starting with Mondays or months), for
each of which the aggregated values are then computed and returned.
Aggregation methods define how to aggregate the values within individual
time periods. The following aggregation methods are supported:
Additionally, periods at the beginning or the end of the time series may
or may not be included if they are incomplete (there isn’t data for each
day in the period). This is controlled by the setting includeIncomplete,
which supports the following values:
Finally, the pull request usage chart is changed to make use of the new
aggregation facilities to reduce the granularity from daily to monthly
data for now. This might be changed when we implement detail views.
I also added several unit tests to check the aggregation methods (for
off-by-one errors in particular).